All python thread(in CPython) are under GIL.
What if the thread is created by ctypes?
For example, python just calls the below function through C Library and the function create a thread in C area not python.
#include<thread>
int createUnitTestThread(int sasAddr){
sasEngine->thread = new std::thread(....);
return 0;
}
Is it the same or not ?
It's not like threads are under the GIL, operations in the Python interpreter are (including stuff like the fetch and execution of most opcodes, so that's why threads that execute Python code run mostly interlocked).
Your C++ thread will run free as long as it doesn't call back functions in the Python interpreter (either user callbacks or functions coming from Python.h).
How can i keep using the console while executing a process from a boost::python module? I figured i have to use threading but I think I'm missing something.
import pk #my boost::python module from c++
import threading
t = threading.Thread(target=pk.showExample, args=())
t.start()
This executes showExample, which runs a Window rendering 3D content. Now i would like to keep on coding in the python console while this window is running. The example above works to show the Window but fails to keep the console interactive. Any Ideas how to do it? Thanks for any suggestions.
Greetings
Chris
Edit: I also tried to make Threads in the showExample() C++ code, didn't work as well. I probably have to make the console a thread, but I have not a clue how and can't find any helpful examples.
Edit2: to make the example more simple I implemented these c++ methods:
void Example::simpleWindow()
{
int running = GL_TRUE;
glfwInit();
glfwOpenWindow(800,600, 8,8,8,8,24,8, GLFW_WINDOW);
glewExperimental = GL_TRUE;
glewInit();
glEnable(GL_DEPTH_TEST);
glEnable(GL_CULL_FACE);
glCullFace(GL_BACK);
while(running)
{
glClearColor(0.0f, 0.0f, 0.0f, 0.0f);
glClear(GL_COLOR_BUFFER_BIT | GL_DEPTH_BUFFER_BIT | GL_STENCIL_BUFFER_BIT);
glfwSwapBuffers();
running = !glfwGetKey(GLFW_KEY_ESC) && gkfwGetWindowParam(GLFW_OPENED);
}
}
void Example::makeWindowThread()
{
boost::thread t(simpleWindow);
t.join();
}
There may be some useless lines of code (it was just copy paste of a part from the real method i want to use.) Both methods are static. If I start interactive console in a thread and start the pk.makeWindowThread() in python, i can't give input anymore. Doesn't work if I put the call of pk.makeWindowThread() in a python thread as well. (I am trying to print something in console while showing the window.
When trying to execute a process while keeping the console interactive, then consider using the subprocess or multiprocessing modules. When doing this within Boost.Python, it is probably more appropriate to execute a process in C++ using execv() family of functions.
When trying to spawn a thread while keeping the console interactive, then one must consider the Global Interpreter Lock (GIL). In short, the GIL is a mutex around the interpreter, preventing parallel operations to be performed on Python objects. Thus, at any point in time, a max of one thread, the one that has acquired the GIL, is allowed to perform operations on Python objects.
For multithreaded Python programs with no C or C++ threads, the CPython interpreter functions as a cooperative scheduler, enabling concurrency. Threads will yield control when Python knows the thread is about to perform a blocking call. For example, a thread will release the GIL within time.sleep(). Additionally, the interpreter will force a thread to yield control after certain criteria have been met. For example, after a thread has executed a certain amount of bytecode operations, the interpreter will force it to yield control, allowing other threads to execute.
C or C++ threads are sometimes referred to as alien threads in the Python documentation. The Python interpreter has no ability to force an alien thread to yield control by releasing the GIL. Therefore, alien threads are responsible for managing the GIL to permit concurrent or parallel execution with Python threads. With this in mind, lets examine some of the C++ code:
void Example::makeWindowThread()
{
boost::thread t(simpleWindow);
t.join();
}
This will spawn a thread, and thread::join() will block, waiting for the t thread to complete execution. If this function is exposed to Python via Boost.Python, then the calling thread will block. As only one Python thread is allowed to be executed at any point in time, the calling thread will own the GIL. Once the calling thread blocks on t.join(), all other Python threads will remain blocked, as the interpreter cannot force the thread to yield control. To enable other Python threads to run, the GIL should be released pre-join, and acquired post-join.
void Example::makeWindowThread()
{
boost::thread t(simpleWindow);
release GIL // allow other python threads to run.
t.join();
acquire GIL // execution is going to occur within the interpreter.
}
However, this will still cause the console to block waiting for the thread to complete execution. Instead, consider spawning the thread and detaching from it via thread::detach(). As the calling thread will no longer block, managing the GIL within Example::makeWindowThread is no longer necessary.
void Example::makeWindowThread()
{
boost::thread(simpleWindow).detach();
}
For more details/examples of managing the GIL, please consider reading this answer for a basic implementation overview, and this answer for a much deeper dive into considerations one must take.
You have two options:
start python with the -i flag, that will cause to drop it to the interactive interperter instead of exiting from the main thread
start an interactive session manually:
import code
code.interact()
The second option is particularily useful if you want to run the interactive session in it's own thread, as some libraries (like PyQt/PySide) don't like it when they arn't started from the main thread:
from code import interact
from threading import Thread
Thread(target=interact, kwargs={'local': globals()}).start()
... # start some mainloop which will block the main thread
Passing local=globals() to interact is necessary so that you have access to the scope of the module, otherwise the interpreter session would only have access to the content of the thread's scope.
Basically there seems to be massive confusion/ambiguity over when exactly PyEval_InitThreads() is supposed to be called, and what accompanying API calls are needed. The official Python documentation is unfortunately very ambiguous. There are already many questions on stackoverflow regarding this topic, and indeed, I've personally already asked a question almost identical to this one, so I won't be particularly surprised if this is closed as a duplicate; but consider that there seems to be no definitive answer to this question. (Sadly, I don't have Guido Van Rossum on speed-dial.)
Firstly, let's define the scope of the question here: what do I want to do? Well... I want to write a Python extension module in C that will:
Spawn worker threads using the pthread API in C
Invoke Python callbacks from within these C threads
Okay, so let's start with the Python docs themselves. The Python 3.2 docs say:
void PyEval_InitThreads()
Initialize and acquire the global interpreter lock. It should be
called in the main thread before creating a second thread or engaging
in any other thread operations such as PyEval_ReleaseThread(tstate).
It is not needed before calling PyEval_SaveThread() or
PyEval_RestoreThread().
So my understanding here is that:
Any C extension module which spawns threads must call
PyEval_InitThreads() from the main thread before any other threads
are spawned
Calling PyEval_InitThreads locks the GIL
So common sense would tell us that any C extension module which creates threads must call PyEval_InitThreads(), and then release the Global Interpreter Lock. Okay, seems straightforward enough. So prima facie, all that's required would be the following code:
PyEval_InitThreads(); /* initialize threading and acquire GIL */
PyEval_ReleaseLock(); /* Release GIL */
Seems easy enough... but unfortunately, the Python 3.2 docs also say that PyEval_ReleaseLock has been deprecated. Instead, we're supposed to use PyEval_SaveThread in order to release the GIL:
PyThreadState* PyEval_SaveThread()
Release the global interpreter lock (if it has been created and thread
support is enabled) and reset the thread state to NULL, returning the
previous thread state (which is not NULL). If the lock has been
created, the current thread must have acquired it.
Er... okay, so I guess a C extension module needs to say:
PyEval_InitThreads();
PyThreadState* st = PyEval_SaveThread();
Indeed, this is exactly what this stackoverflow answer says. Except when I actually try this in practice, the Python interpreter immediately seg-faults when I import the extension module. Nice.
Okay, so now I'm giving up on the official Python documentation and turning to Google. So, this random blog claims all you need to do from an extension module is to call PyEval_InitThreads(). Of course, the documentation claims that PyEval_InitThreads() acquires the GIL, and indeed, a quick inspection of the source code for PyEval_InitThreads() in ceval.c reveals that it does indeed call the internal function take_gil(PyThreadState_GET());
So PyEval_InitThreads() definitely acquires the GIL. I would think then that you would absolutely need to somehow release the GIL after calling PyEval_InitThreads(). But how? PyEval_ReleaseLock() is deprecated, and PyEval_SaveThread() just inexplicably seg-faults.
Okay... so maybe for some reason which is currently beyond my understanding, a C extension module doesn't need to release the GIL. I tried that... and, as expected, as soon as another thread attempts to acquire the GIL (using PyGILState_Ensure), the program hangs from a deadlock. So yeah... you really do need to release the GIL after calling PyEval_InitThreads().
So again, the question is: how do you release the GIL after calling PyEval_InitThreads()?
And more generally: what exactly does a C-extension module have to do to be able to safely invoke Python code from worker C-threads?
Your understanding is correct: invoking PyEval_InitThreads does, among other things, acquire the GIL. In a correctly written Python/C application, this is not an issue because the GIL will be unlocked in time, either automatically or manually.
If the main thread goes on to run Python code, there is nothing special to do, because Python interpreter will automatically relinquish the GIL after a number of instructions have been executed (allowing another thread to acquire it, which will relinquish it again, and so on). Additionally, whenever Python is about to invoke a blocking system call, e.g. to read from the network or write to a file, it will release the GIL around the call.
The original version of this answer pretty much ended here. But there is one more thing to take into account: the embedding scenario.
When embedding Python, the main thread often initializes Python and goes on to execute other, non-Python-related tasks. In that scenario there is nothing that will automatically release the GIL, so this must be done by the thread itself. That is in no way specific to the call that calls PyEval_InitThreads, it is expected of all Python/C code invoked with the GIL acquired.
For example, the main() might contain code like this:
Py_Initialize();
PyEval_InitThreads();
Py_BEGIN_ALLOW_THREADS
... call the non-Python part of the application here ...
Py_END_ALLOW_THREADS
Py_Finalize();
If your code creates threads manually, they need to acquire the GIL before doing anything Python-related, even as simple as Py_INCREF. To do so, use the following:
// Acquire the GIL
PyGILState_STATE gstate;
gstate = PyGILState_Ensure();
... call Python code here ...
// Release the GIL. No Python API allowed beyond this point.
PyGILState_Release(gstate);
There are two methods of multi threading while executing C/Python API.
1.Execution of different threads with same interpreter - We can execute a Python interpreter and share the same interpreter over the different threads.
The coding will be as follows.
main(){
//initialize Python
Py_Initialize();
PyRun_SimpleString("from time import time,ctime\n"
"print 'In Main, Today is',ctime(time())\n");
//to Initialize and acquire the global interpreter lock
PyEval_InitThreads();
//release the lock
PyThreadState *_save;
_save = PyEval_SaveThread();
// Create threads.
for (int i = 0; i<MAX_THREADS; i++)
{
hThreadArray[i] = CreateThread
//(...
MyThreadFunction, // thread function name
//...)
} // End of main thread creation loop.
// Wait until all threads have terminated.
//...
//Close all thread handles and free memory allocations.
//...
//end python here
//but need to check for GIL here too
PyEval_RestoreThread(_save);
Py_Finalize();
return 0;
}
//the thread function
DWORD WINAPI MyThreadFunction(LPVOID lpParam)
{
//non Pythonic activity
//...
//check for the state of Python GIL
PyGILState_STATE gilState;
gilState = PyGILState_Ensure();
//execute Python here
PyRun_SimpleString("from time import time,ctime\n"
"print 'In Thread Today is',ctime(time())\n");
//release the GIL
PyGILState_Release(gilState);
//other non Pythonic activity
//...
return 0;
}
Another method is that, we can execute a Python interpreter in the main thread and, to each thread we can give its own sub interpreter. Thus every thread runs with its own separate , independent versions of all imported modules, including the fundamental modules - builtins, __main__ and sys.
The code is as follows
int main()
{
// Initialize the main interpreter
Py_Initialize();
// Initialize and acquire the global interpreter lock
PyEval_InitThreads();
// Release the lock
PyThreadState *_save;
_save = PyEval_SaveThread();
// create threads
for (int i = 0; i<MAX_THREADS; i++)
{
// Create the thread to begin execution on its own.
hThreadArray[i] = CreateThread
//(...
MyThreadFunction, // thread function name
//...); // returns the thread identifier
} // End of main thread creation loop.
// Wait until all threads have terminated.
WaitForMultipleObjects(MAX_THREADS, hThreadArray, TRUE, INFINITE);
// Close all thread handles and free memory allocations.
// ...
//end python here
//but need to check for GIL here too
//re capture the lock
PyEval_RestoreThread(_save);
//end python interpreter
Py_Finalize();
return 0;
}
//the thread functions
DWORD WINAPI MyThreadFunction(LPVOID lpParam)
{
// Non Pythonic activity
// ...
//create a new interpreter
PyEval_AcquireLock(); // acquire lock on the GIL
PyThreadState* pThreadState = Py_NewInterpreter();
assert(pThreadState != NULL); // check for failure
PyEval_ReleaseThread(pThreadState); // release the GIL
// switch in current interpreter
PyEval_AcquireThread(pThreadState);
//execute python code
PyRun_SimpleString("from time import time,ctime\n" "print\n"
"print 'Today is',ctime(time())\n");
// release current interpreter
PyEval_ReleaseThread(pThreadState);
//now to end the interpreter
PyEval_AcquireThread(pThreadState); // lock the GIL
Py_EndInterpreter(pThreadState);
PyEval_ReleaseLock(); // release the GIL
// Other non Pythonic activity
return 0;
}
It is necessary to note that the Global Interpreter Lock still persists and, in spite of giving individual interpreters to each thread, when it comes to python execution, we can still execute only one thread at a time. GIL is UNIQUE to PROCESS, so in spite of providing unique sub interpreter to each thread, we cannot have simultaneous execution of threads
Sources: Executing a Python interpreter in the main thread and, to each thread we can give its own sub interpreter
Multi threading tutorial (msdn)
I have seen symptoms similar to yours: deadlocks if I only call PyEval_InitThreads(), because my main thread never calls anything from Python again, and segfaults if I unconditionally call something like PyEval_SaveThread(). The symptoms depend on the version of Python and on the situation: I am developing a plug-in that embeds Python for a library that can be loaded as part of a Python extension. The code needs therefore to run independent of whether it is loaded by Python as main.
The following worked for be with both python2.7 and python3.4, and with my library running within Python and outside of Python. In my plug-in init routine, which is executed in the main thread, I run:
Py_InitializeEx(0);
if (!PyEval_ThreadsInitialized()) {
PyEval_InitThreads();
PyThreadState* mainPyThread = PyEval_SaveThread();
}
(mainPyThread is actually some static variable, but I don't think that matters as I never need to use it again).
Then I create threads using pthreads, and in each function that needs to access the Python API, I use:
PyGILState_STATE gstate;
gstate = PyGILState_Ensure();
// Python C API calls
PyGILState_Release(gstate);
To quote above:
The short answer: you shouldn't care about releasing the GIL after calling PyEval_InitThreads...
Now, for a longer answer:
I'm limiting my answer to be about Python extensions (as opposed to embedding Python). If we are only extending Python, than any entry point into your module is from Python. This by definition means that we don't have to worry about calling a function from a non-Python context, which makes things a bit simpler.
If threads have NOT be initialized, then we know there is no GIL (no threads == no need for locking), and thus "It is not safe to call this function when it is unknown which thread (if any) currently has the global interpreter lock" does not apply.
if (!PyEval_ThreadsInitialized())
{
PyEval_InitThreads();
}
After calling PyEval_InitThreads(), a GIL is created and assigned... to our thread, which is the thread currently running Python code. So all is good.
Now, as far as our own launched worker "C"-threads, they will need to ask for the GIL before running relevant code: so their common methodology is as follows:
// Do only non-Python things up to this point
PyGILState_STATE state = PyGILState_Ensure();
// Do Python-things here, like PyRun_SimpleString(...)
PyGILState_Release(state);
// ... and now back to doing only non-Python things
We don't have to worry about deadlock any more than normal usage of extensions. When we entered our function, we had control over Python, so either we were not using threads (thus, no GIL), or the GIL was already assigned to us. When we give control back to the Python run-time by exiting our function, the normal processing loop will check the GIL and hand control of as appropriate to other requesting objects: including our worker threads via PyGILState_Ensure().
All of this the reader probably already knows. However, the "proof is in the pudding". I've posted a very-minimally-documented example that I wrote today to learn for myself what the behavior actually was, and that things work properly. Sample Source Code on GitHub
I was learning several things with the example, including CMake integration with Python development, SWIG integration with both of the above, and Python behaviors with extensions and threads. Still, the core of the example allows you to:
Load the module -- 'import annoy'
Load zero or more worker threads which do Python things -- 'annoy.annoy(n)'
Clear any worker threads -- 'annon.annoy(0)'
Provide thread cleanup (on Linux) at application exit
... and all of this without any crashes or segfaults. At least on my system (Ubuntu Linux w/ GCC).
The suggestion to call PyEval_SaveThread works
PyEval_InitThreads();
PyThreadState* st = PyEval_SaveThread();
However to prevent crash when module is imported, ensure Python APIs to import are protected using
PyGILState_Ensure and PyGILState_Release
e.g.
PyGILState_STATE gstate = PyGILState_Ensure();
PyObject *pyModule_p = PyImport_Import(pyModuleName_p);
PyGILState_Release(gstate);
I feel confuse on this issue too. The following code works by coincidence.
Py_InitializeEx(0);
if (!PyEval_ThreadsInitialized()) {
PyEval_InitThreads();
PyThreadState* mainPyThread = PyEval_SaveThread();
}
My main thread do some python runtime initial work, and create other pthread to handle tasks. And I have a better workaround for this. In the Main thread:
if (!PyEval_ThreadsInitialized()){
PyEval_InitThreads();
}
//other codes
while(alive) {
Py_BEGIN_ALLOW_THREADS
sleep or other block code
Py_END_ALLOW_THREADS
}
You don't need to call that in your extension modules. That's for initializing the interpreter which has already been done if your C-API extension module is being imported. This interface is to be used by embedding applications.
When is PyEval_InitThreads meant to be called?
I'm very confused as to how exactly I can ensure thread-safety when calling Python code from a C (or C++) thread.
The Python documentation seems to be saying that the usual idiom to do so is:
PyGILState_STATE gstate;
gstate = PyGILState_Ensure();
/* Perform Python actions here. */
result = CallSomeFunction();
/* evaluate result or handle exception */
/* Release the thread. No Python API allowed beyond this point. */
PyGILState_Release(gstate);
And indeed, this stackoverflow answer seems to confirm as much. But a commenter (with a very high reputation) says otherwise. The commenter says you should use PyEval_RestoreThread()/PyEval_SaveThread().
The docs seem to confirm this:
PyThreadState* PyEval_SaveThread()
Release the global interpreter lock (if it has been created and
thread support is enabled) and reset the thread state to NULL,
returning the previous thread state (which is not NULL). If the lock
has been created, the current thread must have acquired it. (This
function is available even when thread support is disabled at compile
time.)
void PyEval_RestoreThread(PyThreadState *tstate)
Acquire the global interpreter lock (if it has been created and thread
support is enabled) and set the thread state to tstate, which must not
be NULL. If the lock has been created, the current thread must not have
acquired it, otherwise deadlock ensues. (This function is available even
when thread support is disabled at compile time.)
The way the docs describe this, it seems that PyEval_RestoreThread()/PyEval_SaveThread() is basically a mutex lock/unlock idiom. So it would make sense that before calling any Python code from C, you first need to lock the GIL, and then unlock it.
So which is it? When calling Python code from C, should I use:
PyGILState_Ensure()/PyGILState_Release()
or
PyEval_RestoreThread/PyEval_SaveThread?
And what is really the difference?
First, you almost never want to call PyEval_RestoreThread/PyEval_SaveThread. Instead, you want to call the wrapper macros Py_BEGIN_ALLOW_THREADS/Py_END_ALLOW_THREADS. The documentation is written for those macros, which is why you couldn't find it.
Anyway, either way, you don't use the thread functions/macros to acquire the GIL; you use them to temporarily release the GIL when you've acquired it.
So, why would you ever want to do this? Well, in simple cases you don't; you just need Ensure/Release. But sometimes you need to hold onto your Python thread state until later, but don't need to hold onto the GIL (or even explicitly need to not hold onto the GIL, to allow some other thread to progress so it can signal you). As the docs explain, the most common reasons for this are doing file I/O or extensive CPU-bound computation.
Finally, is there any case where you want to call the functions instead of the macros? Yes, if you want access to the stashed PyThreadState. If you can't think of a reason why you might want that, you probably don't have one.
I am trying to write a C++ class that calls Python methods of a class that does some I/O operations (file, stdout) at once. The problem I have ran into is that my class is called from different threads: sometimes main thread, sometimes different others. Obviously I tried to apply the approach for Python calls in multi-threaded native applications. Basically everything starts from PyEval_AcquireLock and PyEval_ReleaseLock or just global locks. According to the documentation here when a thread is already locked a deadlock ensues. When my class is called from the main thread or other one that blocks Python execution I have a deadlock.
Python> Cfunc1() - C++ func that creates threads internally which lead to calls in "my class",
It stuck on PyEval_AcquireLock, obviously the Python is already locked, i.e. waiting for C++ Cfunc1 call to complete... It completes fine if I omit those locks. Also it completes fine when Python interpreter is ready for the next user command, i.e. when thread is calling funcs in the background - not inside of a native call
I am looking for a workaround. I need to distinguish whether or not the global lock is allowed, i.e. Python is not locked and ready to receive the next command... I tried PyGIL_Ensure, unfortunately I see hang.
Any known API or solution for this ?
(Python 2.4)
Unless you have wrapped your C++ code quite peculiarly, when any Python thread calls into your C++ code, the GIL is held. You may release it in your C++ code (if you want to do some consuming task that doesn't require any Python interaction), and then will have to acquire it again when you want to do any Python interaction -- see the docs: if you're just using the good old C API, there are macros for that, and the recommended idiom is
Py_BEGIN_ALLOW_THREADS
...Do some blocking I/O operation...
Py_END_ALLOW_THREADS
the docs explain:
The Py_BEGIN_ALLOW_THREADS macro opens
a new block and declares a hidden
local variable; the
Py_END_ALLOW_THREADS macro closes the
block. Another advantage of using
these two macros is that when Python
is compiled without thread support,
they are defined empty, thus saving
the thread state and GIL
manipulations.
So you just don't have to acquire the GIL (and shouldn't) until after you've explicitly released it (ideally with that macro) and need to interact with Python in any way again. (Where the docs say "some blocking I/O operation", it could actually be any long-running operation with no Python interaction whatsoever).